Searching topics by highest ranked page in a social networking system

ABSTRACT

Equivalent concepts expressed across multiple domains are matched and associated with a metapage generated by a social networking system. User preferences expressed on multiple domains, represented as pages in a social networking system, may be organized by concept and shared with advertisers, third-party developers, and other users of the social networking system using the metapages generated for the concepts. Aggregated social information may be presented to users of the social networking system viewing a page associated with a metapage. Information presented on external websites may be used to link pages across multiple domains with a metapage generated on the social networking system. A best page may be determined for a concept embodied in multiple pages on the social networking system using a hierarchy of rules. A best page may also be determined for a user based on information about the user.

BACKGROUND

This invention relates generally to social networking, and in particularto searching topics by highest ranked page in a social networkingsystem.

In recent years, social networking systems have made it easier for usersto share their interests and preferences in real-world concepts, such astheir favorite movies, musicians, celebrities, soft drinks, hobbies,spoils teams, and activities. Tools have been designed to create nodeson the social networking system that represent web pages that embodythese real-world concepts on different domains external to the socialnetworking system. As a result, multiple pages may exist aboutequivalent real-world concepts.

At the same time, users of social networking systems have shared theirinterests and engaged with other users of the social networking systemsby expressing their interests in these concepts on web pages ondifferent domains external to the social networking system. The amountof information gathered from users is staggering—information describinginterests in sports, music, movies, and the like. Social networkingsystems have recorded this information to personalize the userexperience, but social networking systems have lacked tools to enablethird-party developers to use this user preference information becauseof the duplicative pages that have been created on equivalent topics.

Specifically, the information available on social networking systemsabout users' interests has not been organized to present a singularobject for equivalent concepts expressed across different domains.Information about users' interests and preferences is very valuable tothird-party developers that seek to drive traffic and increaseengagement with their websites. Advertisers may also benefit from thisinformation in marketing interest-based goods and services to users ofthe social networking system. However, existing systems have notprovided efficient mechanisms of organizing and sharing this valuableuser preference information.

SUMMARY

Equivalent concepts expressed across multiple domains are matched andassociated with a metapage generated by a social networking system. Userpreferences expressed on multiple domains, represented as pages in asocial networking system, may be organized by concept and shared withadvertisers, third-party developers, and other users of the socialnetworking system using the metapages generated for the concepts.Aggregated social information may be presented to users of the socialnetworking system viewing a page associated with a metapage. Informationpresented on external websites may be used to link pages across multipledomains with a metapage generated on the social networking system. Inone embodiment, the information on other external websites associatedwith the metapage may be presented as links on the pages associated withthe metapage. Feedback from users may be used to include or excludepages from being associated with a generated metapage. In oneembodiment, a best page may be determined for a concept embodied inmultiple pages on the social networking system using a hierarchy ofrules. In another embodiment, a best page may be determined for a userbased on information about the user. In yet another embodiment, socialcontext information may be provided on a page associated with a metapagefor a viewing user that shows expressions of interest by other users onthe page and other pages associated with the metapage.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is high level block diagram illustrating a process of enablingpreference portability for users of a social networking system, inaccordance with an embodiment of the invention.

FIG. 2 is a network diagram of a system for enabling preferenceportability for users of a social networking system, showing a blockdiagram of the social networking system, in accordance with anembodiment of the invention.

FIG. 3 is high level block diagram illustrating a page matching modulethat includes various modules for determining matching pages in a socialnetworking system, in accordance with an embodiment of the invention.

FIG. 4 is a flowchart of a process of determining matching pages in asocial networking system, in accordance with an embodiment of theinvention.

FIG. 5 is a flowchart of a process of providing user preferences forpages associated with a metapage in a social networking system, inaccordance with an embodiment of the invention.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION Overview

A social networking system offers its users the ability to communicateand interact with other users of the social networking system, Usersjoin the social networking system and add connections to a number ofother users to whom they desire to be connected. Users of socialnetworking system can provide information describing them which isstored as user profiles. For example, users can provide their age,gender, geographical location, education history, employment history andthe like. The information provided by users may be used by the socialnetworking system to direct information to the user. For example, thesocial networking system may recommend social groups, events, andpotential friends to a user. A social networking system may also enableusers to explicitly express interest in a concept, such as celebrities,hobbies, sports teams, books, music, and the like. These interests maybe used in a myriad of ways, including targeting advertisements andpersonalizing the user experience on the social networking system byshowing relevant stories about other users of the social networkingsystem based on shared interests.

A social graph includes nodes connected by edges that are stored on asocial networking system. Nodes include users and objects of the socialnetworking system, such as web pages embodying concepts, and edgesconnect the nodes. Edges represent a particular interaction between twonodes, such as when a user expresses an interest in a web page about anew movie, “Bridesmaids.” The social graph may record interactionsbetween users of the social networking system as well as interactionsbetween users and objects of the social networking system by storinginformation in the nodes and edges that represent these interactions.Custom graph objects and graph actions may be defined by third-partydevelopers as well as administrators of the social networking system todefine attributes of the graph objects and graph actions, For example, agraph object for a movie may have several defined attributes, such astitle, actors, directors, producers, year, and the like.

Third-party developers may enable users of the social networking systemto express interest in web pages hosted on websites external to thesocial networking system. These web pages may be represented as pageobjects in the social networking system as a result of embedding awidget, a social plugin, programmable logic or code snippet into the webpages, such as an iFrame. Any concept that can be embodied in a web pagemay become a node in the social graph on the social networking system inthis manner. As a result, there may be multiple page objects thatrepresent the same concept, such as a page hosted on the socialnetworking system created by producers of the movie, multiple websitesincluding user-generated reviews of the “Bridesmaids” movie, anencyclopedia article about the “Bridesmaids” movie, a user-generated fanpage of “Bridesmaids” on the social networking system, news articles onexternal websites about the “Bridesmaids” movie, and the movieproduction website for the “Bridesmaids” movie. Aggregating the totalnumber of users of the social networking system that are interested inthe “Bridesmaids” movie, regardless of which specific page object a userexpressed an interest in, provides valuable social information that mayinfluence other users to engage with web pages about the concept.Centralizing information from multiple page objects about a conceptunder a single metapage object provides more social coverage to users ofthe social networking system, potentially influencing them to engagewith pages associated with the metapage object. The total aggregatedinformation for a concept, such as the “Bridesmaids” movie, may bepresented on the pages associated with the metapage,

Identifying page objects that represent equivalent concepts may beperformed using various methods. For example, a third-party web page mayinclude links to other third-party web pages about the same topic. Thesocial networking system may use these links to generate across-referenced list of third-party web pages about the topic. Asanother example, page objects may be matched by attributes of the pageobjects, such as title, actors, and year. In one embodiment, an inexactmatching process may be used, such as fuzzy matching for text thatrecognizes misspellings and using closeness for matching locations inplaces objects that represent geographic locations on a socialnetworking system. Further, user feedback may be used to determinematching page objects. Machine learning, heuristics analysis, andregression analysis may be used in matching page objects and associatingmetapage objects to the matching page objects, as described herein.

FIG. 1 illustrates a high level block diagram of a process of generatinga metapage object in a social networking system, in one embodiment. Thesocial networking system 100 generates a metapage object 102 after pageobjects 104 are found to be a match 108. An association 112 is generatedbetween the page objects 104 and the metapage object 102 after a match108 is determined from analyzing the page objects 104. User profileobjects 106 that represent users of the social networking system 100 maybe associated with page objects 104 based on actions 110 performed onthe page objects 104 by the users. Each user of the social networkingsystem 100 is associated with a specific user profile object 106. Userprofile objects 106 include declarative information about the user thatwas explicitly shared by the user and expressed in an action on objectsin the social networking system 100. In one embodiment, a user profileobject 106 may include thirty or more different data fields, each datafield describing an attribute of the corresponding user of the socialnetworking system 100. A viewing user is associated with a viewing userprofile object 114 in the social networking system 100,

FIG. 1 and the other figures use like reference numerals to identifylike elements. A letter after a reference numeral, such as “104 a,”indicates that the text refers specifically to the element having thatparticular reference numeral. A reference numeral in the text without afollowing letter, such as “104,” refers to any or all of the elements inthe figures bearing that reference numeral (e.g. “104” in the textrefers to reference numerals “104 a,” “104 b,” and/or “104 c” in thefigures). Only three page objects 104 are shown in FIG. 1 in order tosimplify and clarify the description.

Page objects 104 may be analyzed to determine if there is a match 108between the page objects 104. Methods of determining a match 108 betweenpage objects 104 include using a third-party database or website thatincludes links of other third-party web pages about a specific topic togenerate a metapage object 102 and associations 112 with matching pageobjects 104 based on the links to the third-party web pages. The linksto the other pages may represent other page objects 104 in the socialnetworking system 100.

Another method of determining matching page objects 104 includesidentifying shared attributes of page objects 104, such as title,actors, directors, and year for a movie associated with page objects 104and generates a match score. If the generated match score exceeds apredetermined threshold, then the page objects 104 are determined to bea match 108 a. Other methods of determining matching page objects 104include inexact matching, including fuzzy matching that accounts formisspellings and closeness matching to match locations based ongeographic closeness, and using feedback from users and administratorsof pages on the social networking system 100 to make a determination ofa match 108.

Yet another method of determining matching page objects 104 includes asimultaneous matching of the page objects 104. For example, suppose thata first page object representing the “Bridesmaids” movie on an externalwebsite, such as IMDB.com, includes as attributes of the first pageobject the year of the movie and a reference to a second page objectrepresenting the lead actress of the movie, Kristen Wiig. A third pageobject representing the “Bridesmaids” movie on a different externalwebsite, such as rottentomatoes.com, may be simultaneously matched tothe first page object while a fourth object representing the leadactress, Kristen Wiig, on rottentomatoes.com may be simultaneouslymatched to the second object representing the same actress based on thesame lead actress being referenced for the same movies.

After a page object 104 is associated with a metapage object 102,performed actions 110 associated with the page object 104 and that wereperformed by users associated with user profile objects 106 may beaggregated to count the total number of performed actions 110 to beassociated with the metapage object 102, in one embodiment. For example,users may express interest in various pages that are represented by pageobjects 104 in the social networking system 100. As a user expressesinterest in a page, an edge representing a performed action 110 with thepage object 104 is generated by the social networking system 100 toconnect the user profile object 106 associated with the user with thepage object 104. As other users interact with the page represented bythe page object 104, an aggregation of interactions with the page may bedisplayed on the page to identify to the viewing users how popular theweb page has been amongst other viewing users. In one embodiment, thetotal number of interactions, aggregated across matching page objects104 associated with the metapage object 102, may be displayed on a pageassociated with one of the matching page objects 104. As a result, thepopularity of a concept, even though expressed on several pages, may beaccurately represented on each of the pages using a metapage object toaggregate the social information on the pages.

A metapage object 102 associated with matching page objects 104 may alsoused as a central repository for information about a concept, in oneembodiment. For example, an address missing from a page object 104 thatrepresents a business, such as a restaurant, may be obtained fromanother page object 104 that has been determined to be a match. Ametapage may be generated on the social networking system 100 inassociation with a metapage object 102. The metapage may merge selectedcontent from pages associated with page objects 104 that have generatedassociations 112 with the metapage object 102, in one embodiment. Pagesassociated with matching page objects 104 may be listed on the metapagefor the metapage object on the social networking system 100.

In another embodiment, a page determined to be the best page for theconcept may be used as the metapage on the social networking system 100for the metapage object 102. A page on the social networking system 100associated with a metapage object 102 may be used as the metapage forthe metapage object 102 associated with the page objects 104 formatching pages on the social networking system 100 based on the viewinguser's information. For example, a viewing user based in the UnitedStates that searches for “Eiffel” among pages in the social networkingsystem 100 may be looking for a page about “The Eiffel Tower.” On theother hand, a viewing user based in France that performs the same searchmay be looking for a page about “Le Tour Eiffel.” Thus, the best pagefor a metapage object 102 may vary depending on information about theviewing user, in one embodiment.

Similarly, a page may be selected for a viewing user based oninformation about users connected to the viewing user. For example, theviewing user may be connected to users that have expressed interest in apage that has not been determined to be the best page for the metapageobject. As a result, the viewing user may be presented with the page inwhich connected users have expressed interest, overriding the best pagedetermined for the metapage object,

System Architecture

FIG. 2 is a high level block diagram illustrating a system environmentsuitable for enabling preference portability for users of a socialnetworking system, in accordance with an embodiment of the invention.The system environment comprises one or more user devices 202, thesocial networking system 100, a network 204, and external websites 216,In alternative configurations, different and/or additional modules canbe included in the system.

The user devices 202 comprise one or more computing devices that canreceive user input and can transmit and receive data via the network204. In one embodiment, the user device 202 is a conventional computersystem executing, for example, a Microsoft Windows-compatible operatingsystem (OS), Apple OS X, and/or a Linux distribution. In anotherembodiment, the user device 202 can be a device having computerfunctionality, such as a personal digital assistant (PDA), mobiletelephone, smart-phone, etc. The user device 202 is configured tocommunicate via network 204. The user device 202 can execute anapplication, for example, a browser application that allows a user ofthe user device 202 to interact with the social networking system 100.In another embodiment, the user device 202 interacts with the socialnetworking system 100 through an application programming interface (API)that runs on the native operating system of the user device 202, such asiOS 4 and ANDROID,

In one embodiment, the network 204 uses standard communicationstechnologies and/or protocols. Thus, the network 204 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, digital subscriber line(DSL), etc. Similarly, the networking protocols used on the network 204can include multiprotocol label switching (MPLS), the transmissioncontrol protocol/Internet protocol (TCP/IP), the User Datagram Protocol(UDP), the hypertext transport protocol (HTTP), the simple mail transferprotocol (SMTP), and the file transfer protocol (FTP). The dataexchanged over the network 204 can be represented using technologiesand/or formats including the hypertext markup language (HTML) and theextensible markup language (XML), In addition, all or some of links canbe encrypted using conventional encryption technologies such as securesockets layer (SSL), transport, layer security (TLS), and InternetProtocol security (IPsec).

FIG. 2 contains a block diagram of the social networking system 100. Thesocial networking system 100 includes a user profile store 206, a webserver 208, a metapage store 210, a content store 212, an edge store214, a metapage generating module 218, a page matching module 220, ametapage API module 222, a page ranking module 224, and a pageredirection module 226. In other embodiments, the social networkingsystem 100 may include additional, fewer, or different modules forvarious applications. Conventional components such as networkinterfaces, security functions, load balancers, failover servers,management and network operations consoles, and the like are not shownso as to not obscure the details of the system,

The web server 208 links the social networking system 100 via thenetwork 204 to one or more user devices 202; the web server 208 servesweb pages, as well as other web-related content, such as Java, Flash,XML, and so forth. The web server 208 may provide the functionality ofreceiving and routing messages between the social networking system 100and the user devices 202, for example, instant messages, queued messages(e.g., email), text and SMS (short message service) messages, ormessages sent using any other suitable messaging technique. The user cansend a request to the web server 208 to upload information, for example,images or videos that are stored in the content store 212. Additionally,the web server 208 may provide API functionality to send data directlyto native user device operating systems, such as iOS, ANDROID, webOS,and RIM.

A page matching module 220 uses various methods of determining whetherpage objects 104 represent equivalent concepts in the social networkingsystem 100, as described above. After page objects 104 are determined tobe a match by the page matching module 220, a metapage object 102 isgenerated by a metapage generating module 218. Metapage objects 102generated are stored in the metapage store 210. Page objects 104 may bestored as content objects in the content store 212. Page objects 104 maybe associated with external websites 216 as well as pages within thesocial networking system 100.

A metapage API module 222 responds to application programming interface(API) calls for preferences information of users of the socialnetworking system 100. In one embodiment, the metapage API module 222responds to requests for a specific node in the social networking system100 and user interests related to that node. For example, administratorsof an online music streaming website may wish to identify the total userinterest in a new song, “Bad Romance” by Lady Gaga, across multiplemusic websites. As a result, the administrators of the online musicstreaming website may make an API call requesting this information,identifying the song by name and artist. The metapage API module 222 mayrespond with the user preferences information in the format requested bythe online music streaming website.

In another embodiment, the metapage API module 222 responds to requestsfor a specific user's interests as well as the interests of other usersconnected to the user. For example, third-party developers of a movieinformation website may desire to compare user interests in movies thatare currently playing and display the information in a side-by-sidecomparison based on the expressed interest in movies across multipledomains. The movie information website may make API calls to the socialnetworking system 100 requesting this user preference information. Themetapage API module 222 responds to the API calls with the requestedinformation.

User account information and other related information for a user arestored in the user profile store 206. The user profile informationstored in user profile store 206 describes the users of the socialnetworking system 100, including biographic, demographic, and othertypes of descriptive information, such as work experience, educationalhistory, gender, hobbies or preferences, location, and the like. Theuser profile may also store other information provided by the user, forexample, images or videos. In certain embodiments, images of users maybe tagged with identification information of users of the socialnetworking system 100 displayed in an image. The user profile store 206also maintains references to the actions stored in an action log andperformed on objects in the content store 212.

The edge store 214 stores the information describing connections betweenusers and other objects on the social networking system 100. Some edgesmay be defined by users, allowing users to specify their relationshipswith other users. For example, users may generate edges with other usersthat parallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Other edges are generated when usersinteract with objects in the social networking system 100, such asexpressing interest in a page on the social networking system, sharing alink with other users of the social networking system, and commenting onposts made by other users of the social networking system. The edgestore 214 stores edge objects that include information about the edge,such as affinity scores for objects, interests, and other users.

A metapage generating module 218 may populate a newly-generated metapageobject with information extracted from page objects associated with themetapage object. For example, a page object may exist for a gym, but thepage object may be missing content, such as an exact address, contactinformation, and exercise class schedules, while including otherinformation, such, as users that have expressed an interest in the gym.Another page object representing the same gym may include thisinformation as well as reviews of classes. A further page object,representing the brand of the gym, may offer application content notavailable on the other page objects. As such, the metapage objectrepresenting the gym may be populated with all of the informationavailable from the three page objects.

A page ranking module 224 may rank page objects associated with ametapage object to determine the best page for a user, in oneembodiment. For example, page objects describing the Eiffel Tower may beassociated with a metapage object for the Eiffel Tower. Based on theuser's profile information, the page objects describing the Eiffel Towermay be ranked to present the best page for the user. If the user'slanguage preference is set to French, for example, the best page forthat user would be a page object associated with “Le Tour Eiffel,” apage describing the Eiffel Tower in French. Other profile informationabout users of the social networking system, including preferences,geographic locations where users are based, networks in which users areincluded, affinity scores for other users, and behavioral informationrecorded from users' actions on and outside of the social networkingsystem, may be used in ranking the page objects to determine the bestpage for the user.

The page ranking module 224 may be used by other modules in determiningthe best page for a user. Returning to a previous example, suppose thata user performs a search for “Eiffel” on the social networking system.The highest ranked page for that user may be “The Eiffel Tower” becausethe user may be located in the United States, On the other hand, thehighest ranked page for a user located in France may be “Le TourEiffel.” In one embodiment, the search may only return the highestranked page selected for that user, collapsing other pages in a “othersimilar pages” link. In another embodiment, the highest ranked page forthe user may be determined according to preferences of the user'sconnections. Thus, if a user is located in France but is connected tomany users that have expressed an interest in “The Eiffel Tower,” thenthe highest ranked page for that user may be “The Eiffel Tower.” Otherinformation in the user's profile may be used to determine the best pagefor the user, such as language setting, whether other users connected tothe user expressed an interest in a page, and demographic informationmatching other users that expressed an interest in a page.

In another embodiment, the page ranking module 224 ranks a cluster ofpage objects associated with a metapage object to determine the bestpage for that cluster. For example, fans of Justin Bieber mayindividually create pages on the social networking system 100 for JustinBieber, creating a community of pages dedicated to the singer. Inaddition, a page may be created by a business or official entity, e.g.,the publicity firm for Justin Bieber, and encyclopedia entries may berepresented as pages on the social networking system 100, such asWikipedia articles about “Justin Bieber” in English, French, andSpanish. The best page for the cluster may be selected according to ahierarchy of rules, in one embodiment. Authentic pages, owned by abusiness or official entity, are selected first amongst the pagesassociated with the metapage object. Next, pages that are sourced fromthird-party encyclopedic websites, such as Wikipedia, are selected fromthe pages within the viewing user's locale associated with the metapageobject. For example, a page associated with an encyclopedia entry aboutJustin Bieber for users based in the U.S. would be selected for viewingusers located in the U.S. if there were no official page associated withJustin Bieber on the social networking system 100, If there are no pageswithin the viewing user's locale that are sourced from third-partyencyclopedic websites, then pages sourced from third-party encyclopedicwebsites not within the viewing user's locale are next selected as thebest page for the community page. In one embodiment, pages that areassociated with networks and communities, such as network pages for ahigh school, may be selected as the best page if no third-partyencyclopedic pages can be sourced. Finally, inauthentic pages created byusers in the social networking system 100 may be selected as the bestpage for the community. If multiple matching pages exist for each rule,the page with the largest number of fans, i.e. users that have expressedan interest in the page, is selected as the best page. In oneembodiment, a link to the selected best page is provided on each of thepages in the cluster of pages associated with the community page.

In a further embodiment, the page ranking module 224 may select the bestpage for a topic or concept on a third-party website. For example, athird-party website may have multiple pages for a certain topic, such asmultiple versions of pages corresponding to multiple releases of amovie. The best page for that movie on that third-party website may bedetermined by the page ranking module 224 based on third-party developerprovided rules, in one embodiment. In another embodiment, the best pagefor a third-party website may be selected by the social networkingsystem 100 according to the rules outlined above.

In yet a further embodiment, the page ranking module 224 may select thebest page for a viewer of a user's profile page. For example, a user mayexpress an interest in “Baseball,” represented by a page on the socialnetworking system 100. This interest may be provided on the profile pagefor the user, A viewer of the user's profile may have languagepreferences set to Spanish. Thus, “Beisbol,” the Spanish equivalent ofthe “Baseball” interest page, may be selected as the best page for theviewer of the user's profile. As a result, the viewer of the user'sprofile may see that the user is interested in “Beisbol” even though theuser actually expressed an interest in the page for “Baseball” becausethe page for “Beisbol” and the page for “Baseball” are associated with ametapage object for “Baseball.”

A page redirection module 226 may operate in conjunction with othermodules of the social networking system 100 to redirect users to thebest page of the cluster of pages associated with the metapage. Forexample, a user may have expressed an interest in an inauthentic pagefor the movie “Star Warz,” created by a user of the social networkingsystem. As a result, a content item describing this expression ofinterest by the user in “Star Warz” may be shared with other users ofthe social networking system 100, but with an option for the other usersto express an interest in “Star Wars,” a page created by the officialentity for the movie, instead of the page for “Star Warz.” The pageredirection module 226 generates this option in conjunction with modulesof the social networking system 100 that share the content item with theother users connected to the user expressing the interest in “StarWarz.”

In another embodiment, the page redirection module 226 may provide userswith an option to express an interest in the best page for the clusterof pages associated with a metapage object after the users haveexpressed an interest in a page associated with the metapage object. Forexample, a user may express an interest in an external website forselling concert tickets to a local Bon Iver concert in Berkeley, Calif.After the user performed this action on the page associated with the BonIver metapage, the page redirection module 226 may further prompt theuser to express an interest in the best page selected for Bon Iver. Inone embodiment, the social networking system 100 may track userbehaviors on external websites, such as engaging with a website for theconceit tour of Bon Iver for a period of time, purchasing concerttickets, sharing links about the tour, and the like. By tracking theseuser behaviors, the social networking system 100 may infer an interestin Bon Iver and provide the user with a suggestion to express aninterest in the best page selected for Bon Iver, In one embodiment, thebest page selected for a concept may be referred to as a canonicalentity.

In one embodiment, universal social context may be generated for a userviewing pages associated with a metapage object that identifies otherusers connected to the user that have expressed an interest in thosepages. For example, the number of users expressing an interest in a pagefor an external website selling concert tickets to a local Bon Iver showmay be small, under 200 “likes,” or expressions of interest. A viewinguser of the external website may be discouraged that none of the viewinguser's friends on the social networking system are interested inattending the Bon Iver concert. However, universal social context may beused to provide a better understanding of the viewing user's interests.The best page for Bon Iver, determined by the social networking systemas the page owned by an official entity associated with Bon Iver, mayhave a large number of users expressing interest in the band, such asover 600,000 likes, 20 of which are likes by users connected to theviewing user, and 8 of which are likes by users living near the concertvenue. Thus, the 8 users that are connected to the viewing user thathave expressed an interest in the band “Bon Iver” and that live near theconcert venue may be provided in the page for the external websiteselling concert tickets to Bon Iver because the pages are associatedwith the metapage object for Bon Iver.

Universal social context information may also be provided on pages inthe social networking system 100 to viewing users, showing relatedsocial activity for external websites, external systems, and socialnetworking systems. For example, a user viewing the page for Bon Ivermay be provided with songs recently listened to by other users connectedto the user on a music streaming service by Bon Iver, albums purchasedby friends of the user on a digital music website or subscriptionservice, concert reviews posted by other users of the social networkingsystem, and so forth. In one embodiment, social context informationcomprises selected actions performed by other users of the socialnetworking system connected to a viewing user that are ranked accordingto relevancy to the page being viewed by the viewing user. For example,a viewing user that is viewing a page for selling concert tickets to BonIver may be less interested in a link shared by a connected user thatdescribes indie folk music generally and more interested in a concertreview of a recent Bon Iver concert by another connected user. As such,actions may be selected as universal social context information for apage being viewed by a user based on its ranked relevancy to the page.

In another embodiment, actions may be ranked and selected as universalsocial context information according to affinities of the user. Affinitymay be determined by the social networking system by observing userbehavior with other nodes on the social networking system. For example,a user may have a strong affinity for a subset of other users connectedto the user on the social networking system, evidenced by an aboveaverage engagement with the subset of other users. As a result, theactions of the subset of other users connected to the user may be moreinfluential on the user, providing better social context information tothe user for a page.

Universal social context information may also be provided on userprofiles to provide an insight into the shared interests of a user'sconnections. For example, if a user professes an interest in the SanFrancisco Giants, related actions of other users connected to the user,such as check-in events at AT&T Park in San Francisco, links sharedabout players on the team, and social gaming applications installed bythe other users that are related to fantasy baseball teams, may beprovided on the user's profile as universal social context information.In one embodiment, a social context feed may be embedded as a widget onexternal websites. For example, third-party developers for the externalwebsite for the San Francisco Giants may embed a social context feedwidget into the external website so that, as users of a socialnetworking system visit the external website, related actions by otherusers connected to the visiting users may be provided in the socialcontext feed widget.

In addition, universal social context information may be used inredirecting a user to a particular page in which other users connectedto the user expressed an interest by the page redirection module 226.The social context page redirection may be integrated in a social pluginembedded on an external website associated with a different page relatedto the particular page, a content item about another user expressing aninterest in a metapage that is associated with the particular page, aswell as sponsored stories selected as advertisements to users of thesocial networking system.

Determining Matching Pages on a Social Networking System

FIG. 3 illustrates a high level block diagram of the page matchingmodule 220 in further detail, in one embodiment. The page matchingmodule 220 includes an external data gathering module 300, a matchingrules module 302, a user feedback module 304, a regression analysismodule 306, a heuristics analysis module 308, and a machine learningmodule 310. These modules may perform in conjunction with each other orindependently to develop a match scoring model of matching pages on asocial networking system 100.

An external data gathering module 300 interfaces with external websites216 to process information about page objects 104 of the socialnetworking system 100. This information may include content on athird-party website and other data licensed from third-party providers.Links extracted from the third-party website are analyzed to identifyother websites describing the same concept. Third-party data providersmay license information about websites to a social networking systemthat describe equivalent concepts. In one embodiment, the external datagathering module 300 may process the information retrieved from externalwebsites 216 in a batch process asynchronously from the page matchingmodule 220.

A matching rules selection module 302 selects rules for matching pageobjects in the social networking system 100. The page matching module220 may use several matching rules in determining a match score for aplurality of page objects 104, Weights, or coefficients, may be assignedto the matching rules such that a particular rule may have more weightthan another. The matching rules selection module 302 operates inconjunction with the other modules of the page matching module 220 todetermine these weights, in one embodiment. In another embodiment, thematching rules selection module 302 selects which matching rules to usein determining whether page objects represent equivalent concepts,resulting in a match. For example, a list of cross-referenced websitesthat represent the same concept may be generated by the external datagathering module 300. As a result, the page objects associated with thewebsites on the list of cross-referenced websites that represent thesame concept may all be determined to be a match without the need tocalculate a match score because of a heavily weighted rule. In oneembodiment, a match score of 100% would be determined for the pageobjects that are associated with the websites on the list ofcross-referenced websites that represent the same concept.

The matching rules selection module 302 may, in another embodiment,select a set of matching rules for use in a match scoring modeldeveloped by the regression analysis module 306. The selection ofmatching rules may be manually selected by administrators of the socialnetworking system 100, in one embodiment. In another embodiment,matching rules may be selected to be used in the match scoring modelbased on user feedback received via the user feedback module 304 thataffirms or disaffirms a determined match. In yet another embodiment, allof the matching rules may be selected to be incorporated into the matchscoring model.

Matching rules may include, in one embodiment, analyzing attributes ofthe page objects to identify matching attributes. Page objects may beassociated with a graph object type that has been defined to havemultiple attributes, or object properties. For example, a movie objecttype may be defined to have properties such as a director object type, aproducer object type, a lead actor type, and the like. A matching rulemay require that at least two of the attributes of a movie object typebe the same in order for two page objects to be a match. Anothermatching rule may specify which attributes of the graph object type mustmatch, such as the title, year, and lead actor of a movie. Yet anothermatching rule may define a predetermined threshold match score that mustbe met or exceeded based on other matching rules. For example, pageobjects that match on title and year may be assigned a 95% match scorebased on the likelihood that the page object match. This probability maybe calculated by the regression analysis module 306 or by the machinelearning module 310 in observing past matches and user feedback receivedfrom the user feedback module 304, On the other hand, page objects thatmatch only on lead actor and year may be assigned a 38% match score, forexample, based on the number of movies that the lead actor had been castin that year. As a result, the page objects having a 38% match score maynot be determined as a match according to the matching rule defining apredetermined threshold match score.

The regression analysis module 306 uses a regression model to matchpages on the social networking system 100. In one embodiment, a matchscoring model uses regression analysis to determine weights for matchingrules in the match scoring model. For example, an initial weight may beassigned to the matching rule above in which the title, year, and leadactor matched between two page objects. The initial weight may beadjusted up or down based on user feedback received from users affirmingor disaffirming the match using regression analysis. Using a combinationof the matching rules, the regression model assigns a coefficient toeach of the matching rules based on user feedback and probability of amatch. In one embodiment where a metapage object is already associatedwith page objects and a new page object is being analyzed, theregression analysis module 306 determines a match score that indicateswhether a page object is a good fit with other page objects associatedwith the metapage object. A curve fit, or best fit, yields a number from0 to 1 that can be used as the accuracy measurement of the match. Theregression analysis module 306, in one embodiment, adapts the regressionmodel to include or exclude matching rules that are determined to berelevant or not relevant to matching equivalent pages based on machinelearning and heuristics analysis of the representative page objects.

A heuristics analysis module 308 operates independently andasynchronously from the other modules in the page matching module 220.The heuristics analysis module 308 performs various steps to gatherinformation from the social networking system 100. For example, anaction log includes actions that users perform on the social networkingsystem. The heuristics analysis module 308 may be used to analyze thelevel of communications activity for pages on the social networkingsystem to determine whether those communications included certainkeywords, such as “Justin Bieber,” that may indicate the topic of page.

Another use of the heuristics analysis module 308 includes gathering andanalyzing different types of information from the page objects beingcompared. For example, administrators of page objects may post numerouslinks to external websites with information related to the topic of thepage objects, such as a particular locale, such as Oahu, Hi. Theheuristics analysis module 308 may gather and analyze these externalwebsites and conclude that a particular locale was mentioned in all ofthe websites. As a result, the page objects may be associated with ametapage object generated for that locale.

In one embodiment, an assertion may be made by a user of the socialnetworking system that two or more pages represent the same concept. Theheuristics analysis module 308 may keep track of the assertions made byusers and discount assertions made by users that have a low reputationscore based on previous assertions made by those users. For example, anassertion may be made that a page about Manolo Blahnik shoes and a pagededicated to glass slippers may be made by a user as a joke. Theheuristics analysis module 308 may analyze content on both page objectsand determine that they are not a match. Based on the fraudulentassertion, the user's reputation score may be decreased. In the future,that user's reputation score will prevent a metapage from beinggenerated because the user's reputation score did not meet apredetermined threshold reputation score.

In one embodiment, a plurality of metapage objects may be merged when anew metapage is created with existing pages. Various conflict rules maybe used to merge a new metapage object with an existing metapage object,including confidence scores (matching scores) generated by theheuristics analysis module 308. A new metapage object may break old tiesto page objects based on assertions that include reasons for breakingthe old ties, such as inactive page objects and page objects that havelow reputation scores.

A machine learning module 310 may be used in the page matching module220 to refine the match scoring model defined in the matching rulesselection module 302 and the regression analysis module 306. In oneembodiment, a social networking system 100 uses a machine learningalgorithm to analyze user feedback received from the user feedbackmodule to retrain the match scoring model. The match scoring model maybe refined to include more or less matching rules and the weightsassigned to each matching rule, or coefficients, can also be adjustedbased upon the user feedback.

FIG. 4 illustrates a flow chart diagram depicting a process ofgenerating a metapage object in a social networking system, inaccordance with an embodiment of the invention. An assertion that atleast two page objects represent matching concepts is received 402 bythe social networking system 100. This assertion may be made by a userof the social networking system 100, in one embodiment. The assertionincludes a user identifier, identifiers of the page objects, and areason for the match. Information about the page objects is received404. This information may include attributes about the page objects,other object associated with the page objects, external websitesassociated with the page objects, and other information from third-partyproviders. For example, a user may assert that two page objects arematching concepts for “Justin Bieber” even if one page object is titled“We are beliebers!” The reason included in the assertion may be thatboth page objects are fan pages of Justin Bieber.

After the information about pages of the social networking system isreceived 404, a match score is determined 406 according to matchingrules in a match scoring model based on the received information aboutthe page objects. An administrator may, in one embodiment, manuallyselect the matching rules in the match scoring model, as discussedabove, such as matching attributes and selected object properties usingthe page matching module 220. The page matching module 220 may selectmatching rules, in another embodiment, based on the results ofregression analysis and user feedback regarding prior page objects.Returning to the example above, “belieber” may be a keyword that isdirectly associated with fans of Justin Bieber, evidenced by heuristicanalysis of other page objects associated with Justin Bieber, After thisdiscovery, the keyword “belieber” may be added as a manual matchingrule, automatically matching page objects that include the keyword inthe title, for example. In this sense, non-normalized concepts, such asthe page object titled “We are beliebers” and the page object titled“Justin Bieber,” may become normalized concepts, organized around ametapage object for Justin Bieber.

After a match score has been determined 406, a metapage object isgenerated 408 in association with the page objects included in theassertion if the match score exceeds a predetermined threshold. If thematch score does not exceed the threshold, the process ends. Thegenerated metapage object is stored in a database that is indexed by theidentifier for the metapage objects. The generated metapage object alsostores identifiers of the page objects included in the assertion. Inthis way, the generated metapage object may aggregate social informationabout its associated page objects by querying for the identifiers of thepage objects. In one embodiment, an API call may be made from anexternal website to retrieve aggregated social information about pageobjects associated with a generated metapage object.

Enabling Preference Portability for Users of a Social Networking System

FIG. 5 is a flowchart diagram depicting a process of providing userpreferences for pages associated with a metapage in a social networkingsystem, in accordance with an embodiment of the invention. A request fora page object is received 502 from a user device. For example, a webbrowser on a user's personal computer may be loading a web page hostedon an external website 216 outside the social networking system 100. Theweb page may include an embedded code snippet or widget that requeststhe page object for the web page being loaded, such as an encyclopediaarticle about Britney Spears. The widget may not be visible on the webpage, in one embodiment.

Responsive the request received 502 by the social networking system 100,a metapage object associated with the page object in the request isretrieved 504. As an example, the page object associated with theencyclopedia article about Britney Spears may be associated with ametapage object for the singer. Thus, the metapage object for BritneySpears would be retrieved 504 responsive to the request received 502 forthe page object for the encyclopedia article about Britney Spears.

After the metapage object associated with the page object is retrieved504, information about user preferences are retrieved 506 from pageobjects associated with the metapage object. For example, the widgetembedded in the web site encyclopedia article about Britney Spears mayrequest information about the total number of users on the socialnetworking system 100 that have expressed an interest in Britney Spears.This number may be segmented into users that are connected to theviewing user and users that are not connected to the viewing user, inone embodiment. As a result, this numeric information is retrieved 506from page objects associated with the metapage object for BritneySpears. The total number may then be presented on the encyclopediaarticle after the social networking system 100 provides 508 theretrieved user preferences information to the user device responsive tothe request.

In a further embodiment, the widget may request the viewing user'spreference in the metapage object for Britney Spears based oninteractions with page objects associated with the metapage object,Thus, if the viewing user had previously expressed an interest in a pageabout Britney Spears on the social networking system 100 that isrepresented by a page object associated with the metapage object, thenthe viewing user, upon opening the encyclopedia article for BritneySpears, is presented with information that the viewing user has alreadyexpressed an interest in the encyclopedia article on the externalwebsite 216. The viewing user's preference information is retrieved 506from the page object associated with the page about Britney Spears onthe social networking system because the page object is associated withthe metapage object for Britney Spears, The encyclopedia article aboutBritney Spears may present the viewing user's preference informationafter it has been provided 508 to the user device responsive to therequest. Thus, the user's preference for Britney Spears is made portableto the external website 216 hosting the encyclopedia article aboutBritney Spears.

The widget may, in another embodiment, request information about usersconnected to the viewing user interacting with a page object associatedwith Britney Spears. This information may be filtered by page objectsassociated with the metapage object for Britney Spears. For example, ifusers connected to the viewing user have shared a page post by anauthenticated page for Britney Spears, this information may be providedto the widget separately from information about comments made by usersconnected to the viewing user on a website about Britney Spears that hasnot been authenticated. The information may also be filtered by actiontype. Returning to the example, the actions of sharing a page,commenting on a page and expressing interest in a page may be filteredby action type. As a result, the user preferences information aboutusers connected to the viewing user may be retrieved 506 from pageobjects associated with the metapage object. The user preferencesinformation about users connected to the viewing user may then beprovided 508 to the user device responsive to the request.

SUMMARY

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like, Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a non-transitory, tangible computer readable storagemedium, or any type of media suitable for storing electronicinstructions, which may be coupled to a computer system bus.Furthermore, any computing systems referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

Embodiments of the invention may also relate to a product that isproduced by a computing process described herein. Such a product maycomprise information resulting from a computing process, where theinformation is stored on a non-transitory, tangible computer readablestorage medium and may include any embodiment of a computer programproduct or other data combination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

What is claimed is:
 1. A method comprising: maintaining a plurality ofpages on a social networking system that describes a plurality ofoverlapping concepts; clustering the plurality of pages on the socialnetworking system by concept; receiving a query for a concept from auser of the social networking system; determining, for the user, a bestpage of a cluster of pages associated with the queried concept based oninformation about the user; and providing the best page associated withthe queried concept to the user.
 2. The method of claim 1, whereinclustering the plurality of pages on the social networking system byconcept further comprises generating a metapage for each concept, themetapage associated with a cluster of the plurality of pagesrepresenting the concept.
 3. The method of claim 2, wherein determining,for the user, a best page of a cluster of pages associated with thequeried concept based on information about the user further comprises:retrieving a metapage corresponding to the queried concept; retrievingthe cluster of the plurality of pages associated with the metapagecorresponding to the queried concept; and determining a best page fromthe retrieved cluster of the plurality of pages associated with themetapage corresponding to the queried concept based on attributes of theretrieved cluster of the plurality of pages and a plurality of userpreferences retrieved from the information about the user.
 4. The methodof claim 1, wherein receiving a query for a concept from a user of thesocial networking system further comprises: receiving the query for theconcept from the user in a search query on the social networking system.5. The method of claim 1, wherein determining, for the user, a best pageof a cluster of pages associated with the queried concept based oninformation about the user further comprises: retrieving preferencesinformation for other users of the social networking system connected tothe user; and determining the best page of the cluster of pages based onthe retrieved preferences information for the other users of the socialnetworking system connected to the user.
 6. The method of claim 1,wherein determining, for the user, a best page of a cluster of pagesassociated with the queried concept based on information about the userfurther comprises: retrieving a language setting for the user; anddetermining the best page of the cluster of pages based on the retrievedlanguage setting of the user.
 7. The method of claim 1, whereindetermining, for the user, a best page of a cluster of pages associatedwith the queried concept based on information about the user furthercomprises: retrieving preferences information for similar users of thesocial networking system compared to the user; and determining the bestpage of the cluster of pages based on the retrieved preferencesinformation for the similar users of the social networking systemconnected compared to the user.
 8. The method of claim 7, whereinsimilar users of the social networking system compared to the usercomprises users of the social networking system that are in the samedemographic as the user.
 9. The method of claim 7, wherein similar usersof the social networking system compared to the user comprises users ofthe social networking system that share similar affinity scores forinterests as the user.
 10. The method of claim 7, wherein similar usersof the social networking system compared to the user comprises users ofthe social networking system that share behavioral patterns as the user.11. The method of claim 1, wherein determining, for the user, a bestpage of a cluster of pages associated with the queried concept based oninformation about the user further comprises: retrieving a geographiclocation for the user; and determining the best page of the cluster ofpages based on the retrieved geographic location of the user.
 12. Themethod of claim 1, wherein determining, for the user, a best page of acluster of pages associated with the queried concept based oninformation about the user further comprises: retrieving preferencesinformation for the user, the preferences information includinguser-declared interests and inferred interests; and determining the bestpage of the cluster of pages based on the retrieved preferencesinformation for the user.
 13. A method comprising: maintaining metapageson a social networking system, each metapage associated with a pluralityof pages on the social networking system that describes an equivalentconcept; receiving a query for a concept from a user of the socialnetworking system; retrieving a metapage associated with a set of pagescorresponding to the concept; determining, for the user, a best pagefrom among the set of pages corresponding to the concept based oninformation about the user; and providing, as results of the query, thedetermined best page for the user and the set of pages corresponding tothe concept to the user, where the set of pages are collapsed into aselectable link.
 14. The method of claim 13, wherein determining, forthe user, a best page from among the set of pages corresponding to theconcept based on information about the user further comprises:retrieving a plurality of affinity scores for interests related to theconcept in the social networking system for the user; and determiningthe best page based on the retrieved plurality of affinity scores forinterests related to the concept in the social networking system for theuser.
 15. The method of claim 13, wherein determining, for the user, abest page from among the set of pages corresponding to the concept basedon information about the user further comprises: retrieving a pluralityof networks in the social networking system associated with the user;and determining the best page based on the retrieved plurality ofnetworks in the social networking system associated with the user. 16.The method of claim 13, wherein determining, for the user, a best pagefrom among the set of pages corresponding to the concept based oninformation about the user further comprises: retrieving informationabout other users connected to the user, the information includingexpressions of interest in pages of the set of pages corresponding tothe concept; and determining the best page based on the retrievedinformation about other users connected to the user, where the best pageis determined as the page having a highest number of expressions ofinterest by the other users,
 17. The method of claim 13, whereindetermining, for the user, a best page from among the set of pagescorresponding to the concept based on information about the user furthercomprises: retrieving behavioral information about a plurality ofactions of the user inside and outside of the social networking system,the behavioral information including expressions of Interest In pages ofthe set of pages corresponding to the concept and clicks on externalwebsites; and determining the best page based on the retrievedbehavioral information about the user, where the best page is determinedas the page having a highest number of actions by the user.